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Audio classification systems using deep neural networks and an event-driven auditory sensor


Ceolini, Enea; Kiselev, Ilya; Liu, Shih-Chii (2019). Audio classification systems using deep neural networks and an event-driven auditory sensor. In: 2019 IEEE SENSORS, Montreal, QC, Canada, 27 October 2019 - 30 October 2019, Institute of Electrical and Electronics Engineers.

Abstract

We describe ongoing research in developing audio classification systems that use a spiking silicon cochlea as the front end. Event-driven features extracted from the spikes are fed to deep networks for the intended task. We describe a classification task on naturalistic audio sounds using a low-power silicon cochlea that outputs asynchronous events through a send-on-delta encoding of its sharply-tuned cochlea channels. Because of the event-driven nature of the processing, silences in these naturalistic sounds lead to corresponding absence of cochlea spikes and savings in computes. Results show 48% savings in computes with a small loss in accuracy using cochlea events.

Abstract

We describe ongoing research in developing audio classification systems that use a spiking silicon cochlea as the front end. Event-driven features extracted from the spikes are fed to deep networks for the intended task. We describe a classification task on naturalistic audio sounds using a low-power silicon cochlea that outputs asynchronous events through a send-on-delta encoding of its sharply-tuned cochlea channels. Because of the event-driven nature of the processing, silences in these naturalistic sounds lead to corresponding absence of cochlea spikes and savings in computes. Results show 48% savings in computes with a small loss in accuracy using cochlea events.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Language:English
Event End Date:30 October 2019
Deposited On:12 Feb 2020 09:47
Last Modified:27 Jan 2022 01:09
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE SENSORS
ISSN:2168-9229
ISBN:9781728116341
Additional Information:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
OA Status:Green
Publisher DOI:https://doi.org/10.1109/sensors43011.2019.8956592

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